Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints

Bui, Van-Hai; Hussain, Akhtar; Lee, Woon-Gyu; Kim, Hak-Man

doi:10.3390/en12244672

Cited by 2 publications

(4 citation statements)

References 27 publications

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“…In limited power mode, if the amount of power limit is greater than the maximum output power of the WF system, the set-point of WTGs is set to generate the maximum power. By contrast, if the limit power output is less than the maximum output power of WF, the set-point of WTGs need to reduce to balance the required power from TSO and the amount of power reduction for each WTG is calculated based on the amount of mismatch power and the number of WTGs, as shown in equations (18) and (19). In reserve power mode, the set-point of WTGs are determined simply by maintaining the same proportion of reserve capacity for each WTGs, as given in equation (20).…”

Section: ) Set-point Of Wtgs With Grid-code Constraintsmentioning

confidence: 99%

“…The set-point of WTGs in limited power mode are determined by equations (18) and (19). Detailed information about the set-point of WTGs is presented in Table IV in limited power mode from t1 to t2 and the total output power of WF is 30MW.…”

Section: Handling Various Grid-code Constrains From Tsomentioning

confidence: 99%

“…Therefore, this EMS requires a two-way communication system [18]. In many WF systems with a large number of WTGs, the communication network for such WF systems will be very complex and it will significantly increase computational burden on the centralized EMS [19].…”

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confidence: 99%

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Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Bui

Nguyen

2020

IEEE Access

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In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.

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Section: ) Set-point Of Wtgs With Grid-code Constraintsmentioning

confidence: 99%

Section: Handling Various Grid-code Constrains From Tsomentioning

confidence: 99%

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Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Bui

Nguyen

2020

IEEE Access

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“…However, there is limited research work and a knowledge gap related to the design of WPF communication networks, where the assumptions in available articles with few sensor nodes do not represent the complete monitoring data from WPF subsystems including turbines, meteorological towers and substations. As the WPF communication network will play an important role in the future energy management system [21] and the control of largescale WPF [22], this work aims to fill this knowledge gap by providing a reference network model and simulation for wind turbine internal network based on IEC 61400-25 standard. To the best of our knowledge, this is the first work that investigates and compares the performance of four different communication technologies including Ethernet, ZigBee, WiFi and WiMAX for a wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Wireless Network Architecture for Cyber Physical Wind Energy System

et al. 2020

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There is a growing interest to increase the grid integration of large-scale wind power farms (WPF). As most WPFs are located in remote areas where abundant wind resources are available, these sites are lacking communication infrastructures and network coverage which present major obstacles in enabling reliable data transmission between WPFs and their control centers. With the absence of unified communication network architecture, different vendors and manufacturers are developing their own monitoring and control solutions according to their needs. There is a knowledge gap related to the design of WPF communication networks, where the assumptions of available articles do not represent the complete monitoring data from WPF subsystems including wind turbines, meteorological towers and substations. This work aims to design a wireless network architecture for the grid integration of cyber physical wind energy system based on the IEC 61400-25 standard. The proposed architecture consists of four layers: a wind farm layer, a data acquisition layer, a communication network layer and an application layer. Wireless communication technologies outperform conventional wired-based solutions by offering lower costs, greater flexibility and easier deployment. Based on IEC 61400-25 standard, a wireless turbine area network is proposed for collecting sensing data from wind turbine parts, and connected to a wireless farm area network developed for communication between the remote control center and wind turbines. The network performance of the proposed wireless wind turbine internal network (includes the number of sensor nodes, data types and data size) is evaluated considering different wireless technologies (ZigBee, WiFi and WiMAX) in view of end-to-end delay, wireless channel capacity, and data loss. The simulation results show that wireless-based solutions can meet the delay requirements of the IEEE 1646 standard. This work contributes for building a redundant wireless communication infrastructure for remote monitoring of WPFs with scalable coverage and capacity.

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Hybrid Energy Management System for Operation of Wind Farm System Considering Grid-Code Constraints

Cited by 2 publications

References 27 publications

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Distributed Operation of Wind Farm for Maximizing Output Power: A Multi-Agent Deep Reinforcement Learning Approach

Wireless Network Architecture for Cyber Physical Wind Energy System

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